Primary analysis (localizers used to identify ROIs, extraction from VOE data)
Path: -
input_data/ROI_analysis_outputs_Apr06_2023_convertVectors.csv
Note - the size of some datasets of this type was too big for the
codebook package, so I filtered it down to just the data
from the top 100 voxels.
ROI_data <- rio::import(here("input_data/ROI_analysis_outputs_Apr06_2023_convertVectors.csv")) %>%
filter(top_voxel_selection_method == "100")
var_label(ROI_data) <- list(
top_voxel_selection_method = "Size of extracted ROI (# voxels)",
ROI_name = "",
ROI_category = "Psychology, physics, early visual, or multiple demand ROI",
n_voxels_in_brainmasked_parcel = "N voxels in parcel",
subjectID = "",
contrast_for_topVoxel_selection = "Contrast basis for ROI selection",
selection_contrast_task_name = "Task basis for ROI selection",
extracted_copes_main_condition = "Which condition copes were extracted from",
extracted_run_number = "Which run (1-4) copes were extracted from",
mean_topVoxels_main_condition_copes = "Mean response from ROI",
vector1_topVoxels_main_condition_copes = "Vector of responses in ROI"
)
metadata_list(ROI_data)
## $`@context`
## [1] "http://schema.org/"
##
## $`@type`
## [1] "Dataset"
##
## $variableMeasured
## $variableMeasured[[1]]
## $variableMeasured[[1]]$name
## [1] "top_voxel_selection_method"
##
## $variableMeasured[[1]]$description
## [1] "Size of extracted ROI (# voxels)"
##
## $variableMeasured[[1]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[2]]
## $variableMeasured[[2]]$name
## [1] "ROI_name"
##
## $variableMeasured[[2]]$description
## [1] ""
##
## $variableMeasured[[2]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[3]]
## $variableMeasured[[3]]$name
## [1] "ROI_category"
##
## $variableMeasured[[3]]$description
## [1] "Psychology, physics, early visual, or multiple demand ROI"
##
## $variableMeasured[[3]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[4]]
## $variableMeasured[[4]]$name
## [1] "n_voxels_in_brainmasked_parcel"
##
## $variableMeasured[[4]]$description
## [1] "N voxels in parcel"
##
## $variableMeasured[[4]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[5]]
## $variableMeasured[[5]]$name
## [1] "n_voxels_subject_specific_brainmasked_parcel"
##
## $variableMeasured[[5]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[6]]
## $variableMeasured[[6]]$name
## [1] "subjectID"
##
## $variableMeasured[[6]]$description
## [1] ""
##
## $variableMeasured[[6]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[7]]
## $variableMeasured[[7]]$name
## [1] "contrast_for_topVoxel_selection"
##
## $variableMeasured[[7]]$description
## [1] "Contrast basis for ROI selection"
##
## $variableMeasured[[7]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[8]]
## $variableMeasured[[8]]$name
## [1] "selection_contrast_task_name"
##
## $variableMeasured[[8]]$description
## [1] "Task basis for ROI selection"
##
## $variableMeasured[[8]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[9]]
## $variableMeasured[[9]]$name
## [1] "extracted_copes_main_condition"
##
## $variableMeasured[[9]]$description
## [1] "Which condition copes were extracted from"
##
## $variableMeasured[[9]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[10]]
## $variableMeasured[[10]]$name
## [1] "extracted_run_number"
##
## $variableMeasured[[10]]$description
## [1] "Which run (1-4) copes were extracted from"
##
## $variableMeasured[[10]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[11]]
## $variableMeasured[[11]]$name
## [1] "mean_topVoxels_main_condition_copes"
##
## $variableMeasured[[11]]$description
## [1] "Mean response from ROI"
##
## $variableMeasured[[11]]$`@type`
## [1] "propertyValue"
##
##
## $variableMeasured[[12]]
## $variableMeasured[[12]]$name
## [1] "vector1_topVoxels_main_condition_copes"
##
## $variableMeasured[[12]]$description
## [1] "Vector of responses in ROI"
##
## $variableMeasured[[12]]$`@type`
## [1] "propertyValue"
##
##
##
## $description
## [1] "\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:--------------------------------------------|:---------------------------------------------------------|---------:|\n|top_voxel_selection_method |Size of extracted ROI (# voxels) | 0|\n|ROI_name | | 0|\n|ROI_category |Psychology, physics, early visual, or multiple demand ROI | 0|\n|n_voxels_in_brainmasked_parcel |N voxels in parcel | 0|\n|n_voxels_subject_specific_brainmasked_parcel |NA | 0|\n|subjectID | | 0|\n|contrast_for_topVoxel_selection |Contrast basis for ROI selection | 0|\n|selection_contrast_task_name |Task basis for ROI selection | 0|\n|extracted_copes_main_condition |Which condition copes were extracted from | 0|\n|extracted_run_number |Which run (1-4) copes were extracted from | 0|\n|mean_topVoxels_main_condition_copes |Mean response from ROI | 0|\n|vector1_topVoxels_main_condition_copes |Vector of responses in ROI | 0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2)."
metadata(ROI_data)$name <- "ROI data, Exp 2"
metadata(ROI_data)$description <- "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(ROI_data)$datePublished <- "2023-08-21"
metadata(ROI_data)$creator <- list(
"@type" = "Person",
givenName = "Shari", familyName = "Liu",
email = "shariliu@jhu.edu",
affiliation = list("@type" = "Organization",
name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(ROI_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(ROI_data)$temporalCoverage <- "2023"
codebook(ROI_data)
## No missing values.
Metadata
Description
Dataset name: ROI data, Exp 2
ROI data from Exp2 in the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.
Metadata for search engines
Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:
| name | value |
|---|---|
| @type | Person |
| givenName | Shari |
| familyName | Liu |
| shariliu@jhu.edu | |
| affiliation | Organization , Johns Hopkins University, Baltimore, MD, USA |
|
#Variables
top_voxel_selection_method
Size of extracted ROI (# voxels)
Distribution
Distribution of values for top_voxel_selection_method
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| top_voxel_selection_method | Size of extracted ROI (# voxels) | numeric | 0 | 1 | 100 | 100 | 100 | 100 | 0 | ▁▁▇▁▁ |
ROI_name
Distribution
Distribution of values for ROI_name
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| ROI_name | character | 0 | 1 | 48 | 0 | 34 | 79 | 0 |
ROI_category
Psychology, physics, early visual, or multiple demand ROI
Distribution
Distribution of values for ROI_category
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| ROI_category | Psychology, physics, early visual, or multiple demand ROI | character | 0 | 1 | 4 | 0 | 2 | 12 | 0 |
n_voxels_in_brainmasked_parcel
N voxels in parcel
Distribution
Distribution of values for n_voxels_in_brainmasked_parcel
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| n_voxels_in_brainmasked_parcel | N voxels in parcel | numeric | 0 | 1 | 347 | 1736 | 6411 | 2279.625 | 1431.958 | ▇▆▆▂▁ |
n_voxels_subject_specific_brainmasked_parcel
Distribution
Distribution of values for n_voxels_subject_specific_brainmasked_parcel
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| n_voxels_subject_specific_brainmasked_parcel | numeric | 0 | 1 | 347 | 1731 | 6411 | 2278.638 | 1431.775 | ▇▆▆▂▁ | NA |
subjectID
Distribution
Distribution of values for subjectID
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| subjectID | character | 0 | 1 | 32 | 0 | 15 | 16 | 0 |
contrast_for_topVoxel_selection
Contrast basis for ROI selection
Distribution
Distribution of values for contrast_for_topVoxel_selection
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| contrast_for_topVoxel_selection | Contrast basis for ROI selection | character | 0 | 1 | 5 | 0 | 11 | 22 | 0 |
selection_contrast_task_name
Task basis for ROI selection
Distribution
Distribution of values for selection_contrast_task_name
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| selection_contrast_task_name | Task basis for ROI selection | character | 0 | 1 | 3 | 0 | 4 | 6 | 0 |
extracted_copes_main_condition
Which condition copes were extracted from
Distribution
Distribution of values for extracted_copes_main_condition
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| extracted_copes_main_condition | Which condition copes were extracted from | character | 0 | 1 | 9 | 0 | 8 | 16 | 0 |
extracted_run_number
Which run (1-4) copes were extracted from
Distribution
Distribution of values for extracted_run_number
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| extracted_run_number | Which run (1-4) copes were extracted from | character | 0 | 1 | 10 | 0 | 4 | 17 | 0 |
mean_topVoxels_main_condition_copes
Mean response from ROI
Distribution
Distribution of values for mean_topVoxels_main_condition_copes
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mean_topVoxels_main_condition_copes | Mean response from ROI | numeric | 0 | 1 | -21 | 2 | 43 | 2.808981 | 3.584287 | ▁▇▂▁▁ |
vector1_topVoxels_main_condition_copes
Vector of responses in ROI
Distribution
Distribution of values for vector1_topVoxels_main_condition_copes
0 missing values.
Summary statistics
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| vector1_topVoxels_main_condition_copes | Vector of responses in ROI | character | 0 | 1 | 121527 | 0 | 1170 | 1434 | 0 |